(1) Field of Invention
The present invention relates to robot controllers and, more particularly, to method for adaptive obstacle avoidance for articulated redundant robot arms.
(2) Description of Related Art
A variety of methods for controlling robot arms are known in the art [see references 1, 2, 3, and 4, cited below]. The most notable of such methods is the “Direction-to-Rotation Transform” (DIRECT) robot controller model disclosed in [1 and 2]. The DIRECT model develops relationships between joint angles and arm end locations by a random movement learning process known as “babbling.” Babbling is similar to the strategy used by infants when learning to control their limbs. It involves making a series of random muscle motions and observing the resulting change in arm location. Over time, enough muscle-motion to arm-location relationships are learned that the infant can reach any desired location in the physical vicinity by retrieving the proper joint change from muscle memory. In the case of a robot controller, the system maps changes in joint angles to changes in end-effector locations during the babbling period. After building an extensive knowledge database of joint angle to end-effector location relationships, the system can reach any location in the physical vicinity by retrieving the proper joint angle changes necessary to reach that location. The DIRECT model is fault tolerant and scalable to large degrees of freedom. However, the DIRECT model lacks the ability to avoid obstacles while reaching for targets. This makes the DIRECT model impractical for use in environments containing obstacles.
Another method known in the art for controlling robot arms relies on known trajectories comprising readily available geometries and absolute joint angles [3]. The trajectory to the target serves as the attractor. The influence of each obstacle in the environment is superimposed on the attractor dynamics to modify the path to the target. The reliance of this system on prescribed trajectories makes it highly dependent on system parameters and difficult to implement. Also, the use of absolute joint angles results in poor fault tolerance and poor scalability to robots with large redundancy and perturbations.
Thus, a continuing need exists for a method of obstacle avoidance for robot arms that is fault-tolerant, scalable, and easily implemented in environments containing obstacles.
(3) References Cited
[1] D. Bullock, S. Grossberg, and F. H. Guenther, “A Self-Organizing Neural Model of Motor Equivalent Reaching and Tool Use by a Multi joint Arm,” Journal of Cognitive Neuroscience, vol. 5, pp. 408-435, 1993.
[2] N. Srinivasa and S. Grossberg, “A Self-Organizing Neural Model for Fault Tolerant Control of Redundant Robots,” in Proc. of the IEEE International Joint Conference on Neural Networks, pp. 1146-1152, 2007.
[3] Iossifidis, I and G. Schoner, “Dynamical Systems Approach for the Autonomous Avoidance of Obstacles and Joint-Limits for an Redundant Robot Arm,” Proc. of IEEE 2006 International Conference on Intelligent Robots and Systems, IROS'06, Beijing, China, October 2006.
[4] B. R. Fajen, W. H. Warren, S. Temizer and L. P. Kaelbling, “A Dynamical Model of Visually-guided Steering, Obstacle Avoidance, and Route Selection,” International Journal of Computer Vision, pp. 13-34, 2003.